Electricity price forecasting is a challenging task for decision-makers in deregulated power markets due to the … Deep Learning model to forecast one-step, two-step, and three-step ahead electricity prices based on a Convolutional Neural … Ontario electricity market to assess the model. The results indicate that the proposed model reduced the forecasting error …

, shrinkage and forecast combinations …The daily average price of electricity represents the price of electricity to be delivered over the full next day and … serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for …

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest … proposes a new univariate hybrid model, trained, and tested on German electricity market data, based on the Seasonal Auto … and actual prices. The ability to predict the dynamics of the price of electricity on the spot market is an important …

decentralized Western European market. To that end, several price forecasting methods including autoregressive approaches, multiple … influential fundamental factors are determined and performance of forecasting techniques is analysed with respect to the age of … the market, its degree of liberalization, and the level of volatility. A comparison of Southeast European electricity …

Short-term electricity price forecasting has received considerable attention in recent years. Despite this increased … electricity market (SEM). We utilized several forecasting approaches ranging from standard conditional volatility models to … interest, the literature lacks a concrete consensus on the most suitable forecasting approach. This study reports an extensive …

undertaken to assess the impact of power industry disruptors on the near-term prospect of the electricity demand in the most … electricity - the macro-economic risks, the global risks, and the policy risks; secondly the risks internal to the electricity … portfolio diversification by centralized power generation capitalists. Part Two employs an error correction model to forecast …

distribution forecast accuracy. The application for German electricity prices 2015 reveal that: (i) An autoregressive specification …We present a stochastic modelling approach to describe the dynamics of hourly electricity prices. The suggested … electricity spot prices. The basic idea is to analyze day-ahead prices as panel of 24 cross-sectional hours and to identify …

the electric load, for the forecast of future demand. Here we utilize time series models of the auto-regressive moving …

Electricity demand is modeled as a time-varying parameters (TVP) vector autoegression with or without imposing … RMSE and MAE, and compared trough the Diebold Mariano statistic. On the other hand, forecast intervals of Bayesian models …